Different methods have been proposed for key frame extraction, including color-based methods [18], methods based on motion vectors [19, 20], object-based techniques [21, 22], and methods based on feature vector space [23, 24] to name a few.
Instead of segmenting based on motion at the frame level only, regions which have been divided based on color, motion, and position are tracked.
Features based on motion and periodicity characteristics.
Their segmentation approach involves segmenting the image based on color and motion independently.
(A) Fault plane solutions derived in this study (based on first motion polarities), color coded by their mechanism (black = thrust mechanism, blue = strike-slip mechanism, red = normal mechanism) with picked polarities plotted on the focal mechanisms (black dots correspond to positive polarities; white dots correspond to negative polarities), labeled as shown in Table 3.
During object tracking, segmentation quality measures based on object color and motion contrast are taken.
Thus, unlike much existing work on human activity analysis, the proposed approach is based on general color and motion processing methods, and not on specific models of the human body and its kinematics.
In [19], moving shadows were detected by using its motion characteristics and the underlying physics based on the color segmentation, this method had poor accuracy since it could not distinguish shadows from black objects.
In this study, color mixing is conducted by combining two and three colors based on color mixing theory.
Objects are validated based on RGB, Lab color space, and color deconvolution data.
